## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
# Load required packages for the vignette
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tibble))
suppressPackageStartupMessages(library(knitr))

# We explicitly reference fmi:: functions for clarity
set.seed(123) # for reproducibility

# Apply a mean_shift of 1.0 to FPC 1 for Group B
sim_data <- fmi::simulate_fmi_data(
  N_A = 50, 
  N_B = 50, 
  mean_shift = 1.0, 
  seed = 456
)

# Check data structure
str(sim_data)

## -----------------------------------------------------------------------------
# Run the FMI test with n_perms=9 (to pass CRAN checks)
fmi_results <- fmi::run_fmi(
  Y_mat = sim_data$Y_mat,
  group_vec = sim_data$group_vec,
  argvals = sim_data$argvals,
  n_perms = 9, # Use 499+ for actual research
  progress = FALSE # Turn off progress bar for vignette
)


## -----------------------------------------------------------------------------
# Check if fmi_results$scalar$passed is TRUE
if (!is.null(fmi_results$scalar) && fmi_results$scalar$passed) {
  
  message("Scalar invariance established. Comparing latent means.")
  mean_diff_results <- fmi::compare_latent_means(
    fmi_results = fmi_results,
    group_vec = sim_data$group_vec
  )
  
  print(mean_diff_results)
  
} else {
  message("Scalar invariance rejected. Latent mean comparison aborted.")
}


## -----------------------------------------------------------------------------
# Load data (requires 'refund' package)
if (!requireNamespace("refund", quietly = TRUE)) {
  knitr::knit_exit() # Stop if refund is not installed
}
data(DTI, package = "refund")

# 1. Pre-process the data (first visit, remove NA, set groups)
dti_meta_data <- tibble::tibble(
  ID = DTI$ID,
  visit = DTI$visit,
  case = factor(DTI$case, levels = c(0, 1), labels = c("Control", "Patient"))
)

dti_cca_data <- tibble::as_tibble(DTI$cca) %>%
  setNames(paste0("cca_", 1:ncol(DTI$cca)))

dti_filtered_df <- dplyr::bind_cols(dti_meta_data, dti_cca_data) %>%
  dplyr::filter(visit == 1) %>%
  dplyr::select(ID, case, dplyr::starts_with("cca_")) %>%
  tidyr::drop_na() # Use tidyr::drop_na

# 2. Prepare inputs for FMI
Y_mat <- dti_filtered_df %>%
  dplyr::select(dplyr::starts_with("cca_")) %>%
  as.matrix()

group_vec <- dti_filtered_df$case
argvals <- seq(0, 1, length.out = ncol(Y_mat))

# 3. Run FMI test (n_perms=9 for CRAN)
fmi_dti_results <- fmi::run_fmi(
  Y_mat = Y_mat,
  group_vec = group_vec,
  argvals = argvals,
  n_perms = 9, # Use 499+ for actual research
  progress = FALSE
)

# 4. Compare latent means (if invariance holds)
if (!is.null(fmi_dti_results$scalar) && fmi_dti_results$scalar$passed) {
  message("\n[DTI] Scalar invariance established. Comparing latent means.")
  fmi::compare_latent_means(fmi_dti_results, group_vec)
} else {
  message("\n[DTI] Scalar invariance rejected. Latent mean comparison aborted.")
}


## -----------------------------------------------------------------------------
# Check if 'fda' package is installed
if (!requireNamespace("fda", quietly = TRUE)) {
  message("Package 'fda' needed for the Growth example. Skipping.")
  knitr::knit_exit()
}

# Run the pre-packaged growth example
# (n_perms=9 for CRAN check speed)
fmi_growth_results <- fmi::run_growth_example(n_perms = 9)